A method for compressing computer-generated hologram using genetic algorithm optimized quantum-inspired neural network

Jingyuan Ma; Guanglin Yang; Haiyan Xie

doi:10.1117/12.2602666

9 October 2021 A method for compressing computer-generated hologram using genetic algorithm optimized quantum-inspired neural network

Jingyuan Ma, Guanglin Yang, Haiyan Xie

Author Affiliations +

Proceedings Volume 11898, Holography, Diffractive Optics, and Applications XI; 118981Z (2021) https://doi.org/10.1117/12.2602666
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Conference Poster

Abstract

A method for compressing computer-generated holograms (CGHs) using genetic algorithm optimized quantum-inspired neural network is proposed. Genetic algorithm is a global optimization algorithm, which can provide better initial weights for the quantum-inspired neural network. The global optimization ability of genetic algorithm is combined with the local optimization ability of the quantum-inspired neural network enables the network to achieve better convergence effects. Under different compression ratios, CGHs are compressed by the genetic algorithm optimized quantum-inspired neural network and the quantum-inspired neural network respectively, and Fresnel transform technology is used to reconstruct the decompressed CGHs. The experimental results show that the genetic algorithm optimized quantuminspired neural network can obtain better quality reconstructed images than the quantum-inspired neural network while using fewer learning iterations.

Citation Download Citation

Jingyuan Ma, Guanglin Yang, and Haiyan Xie "A method for compressing computer-generated hologram using genetic algorithm optimized quantum-inspired neural network", Proc. SPIE 11898, Holography, Diffractive Optics, and Applications XI, 118981Z (9 October 2021); https://doi.org/10.1117/12.2602666

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